Use of AI

Of course, you are allowed to use tools like ChatGPT, Elicit, Copilot, or Canva to facilitate your tasks, to have content explained again, or to receive suggestions and inspiration, just as we do.

However, in order to ensure that our work adheres to the principles of good scientific practice and to clarify our expectations regarding the use of these tools, we have compiled a few points below that should be considered.

Responsibility for Content

Everything you submit, send, or publish under your name is your responsibility. This means that you are fully accountable for the content. Specifically, this entails:

You have to at least have a basic understanding of how AI models function (what happens in the background) in order to properly evaluate and interpret the output you receive.
Be aware of potential biases that may affect the answers due to training datasets or algorithms.

Example for bias resulting from training data

Only outsource tasks where you can take responsibility for the content, fact-check everything, and have sufficient prior knowledge of the topic

Ethical AI

Potential Reproduction of Biases

This issue arises from the way data and algorithms are used to train AI models. If the data used to train these models is biased or reflects social inequalities, the resulting AI model will also show these biases. This can lead to AI models reinforcing and perpetuating existing prejudices and discriminatory practices without being noticed.

Model prompt: Computational Social Scientist

Environmental Impacts

Large-scale AI deploymens are hosted in data centers, which have a significant toll on the planet [@AIHasEnvironmental2024].

The Chemetall Foot Lithium Operation in Clayton Valley, Nevada (Image by PDTillman, Wikipedia Commons)

For example:

Producing a 2 kg computer requires about 800 kg of raw materials.
Microchips that power AI require rare earth elements, which are often mined in environmentally destructive ways and frequently come from regions affected by civil-wars.
The production of electronics involves materials like lead and mercury, which are harmful to the environment.
Data centers use water during construction and in operation to cool electronic components. Globally, AI-related infrastructure consumes about six times more water than Denmark, which is a problem, considering a quarter of humanity already lacks access to clean water and sanitation.
The use of fossil fuels contributes to the production of greenhouse gases.
A request made through ChatGPT consumes about 10-times the electricity of a Google search.

Reflection

What is your attitude on the usage of generative AI in class? What are further issues we encounter, when we are tolerating (or not tolerating) AI in class?
What can we do together to ensure an appropriate behavior with AI in class?

Collection of AI-Tools

If you know and use further cool AI-Applications, feel free to share them with us (and we will add them to the list)!

AI-Tools for academic research
Tool	Function	Version	Usage
ChatGPT			Coding Writing Tutoring
Canvas
Elicit	Exploring research papers		Literature review
Notion	All-in-one workspace	Free Version for students	Organizing Writing Leos favorite
GitHub Copilot	Coding Assistant	Free Version for Students	Code Completion Debugging
Gemini	Google`s AI Chatbot		Writing Research Coding Image Generation
ChatAI	Locally hosted DSGVO-conform LLM	Free for Students
Scite.ai	Exploring research papers		Research Citation Analysis
Connected Papers	Research Paper Visualisation	Free	Finding related papers Literature review
Obsidian.md	Knowledge management markdwon tool	Free & Paid	Note taking Organization
theresanaiforthat.com	Repository of AI apps	Free	Search for AI tools based on specific needs